Feasible intervention combinations for achieving a safe exit of the Zero-COVID policy in China and its determinants: an individual-based model study

Background Although several pathways have been proposed as the prerequisite for a safe phase-out in China, it is not clear which of them are the most important for keeping the mortality rate low, what thresholds should be achieved for these most important interventions, and how the thresholds change with the assumed key epidemiological parameters and population characteristics. Methods We developed an individual-based model (IBM) to simulate the transmission of the Omicron variant in the synthetic population, accounting for the age-dependent probabilities of severe clinical outcomes, waning vaccine-induced immunity, increased mortality rates when hospitals are overburdened, and reduced transmission when self-isolated at home after testing positive. We applied machine learning algorithms on the simulation outputs to examine the importance of each intervention parameter and the feasible intervention parameter combinations for safe exits, which is defined as having mortality rates lower than that of influenza in China (14.3 per 100, 000 persons). Results We identified vaccine coverage in those above 70 years old, number of ICU beds per capita, and the availability of antiviral treatment as the most important interventions for safe exits across all studied locations, although the thresholds required for safe exits vary remarkably with the assumed vaccine effectiveness, as well as the age structure, age-specific vaccine coverage, community healthcare capacity of the studied locations. Conclusions The analytical framework developed here can provide the basis for further policy decisions that incorporate considerations about economic costs and societal impacts. Achieving safe exits from the Zero-COVID policy is possible, but challenging for China’s cities. When planning for safe exits, local realities such as the age structure and current age-specific vaccine coverage must be taken into consideration. Supplementary Information The online version contains supplementary material available at 10.1186/s12879-023-08382-x.

. Parameter values for the sensitivity analysis Table S3. Age-dependent transition rates when not vaccinated Table S4. Reduction in the transition rates by vaccine dose Table S5. The distribution of waiting times between states S1 Text. Method for estimating the force of infection The force of infection was estimated as !,# = 0 · 75 $ ! Σ %&' ( " where 0 · 75 $ ! represents the reduced transmission for a given mask coverageaccording to a community study(1) (See Interventions in the main text for details); # is the total number of infectious individuals in the synthetic population at time t; ) is the transmission probability per contact and was calibrated to have a R0 of 7 for the Omicron variant according to previous studies (R0 was set to 5 and 10 in the sensitivity analyses, R0 = 5 and R0 = 10 scenarios in Table S2);(2-5) * # ,* $ is the average number of contacts per day between the susceptible i and the case j (whose ages are ai and aj, respectively) at all locations when j is not identified, or only at home setting when j is identified and selfisolated at home; (6) the are the scaling parameters which vary between zero and one and thereby reduce the force of infection depending on the vaccination status, age groups, symptoms, and ascertainment status of the cases or the susceptibles (Table ST1.1); and Δ = 1/6 day is the time step of the simulation. The vaccine induced moderator of infection risk and onward transmission were represented by + # and + $ , respectively. Therefore, 1-+ # and 1-+ $ represents the VEs against infection and onward transmission. The VEs in the baseline scenario were estimated as the mean of the optimistic and pessimistic scenarios from (5). We assumed that the vaccine-induced immunity against infection, symptom, hospitalization, ICU care and death wanes at a constant rate over time until reaching a certain percentage of the original VEs (Table ST1.2) according to a meta-analysis, (7) while that against onward transmission did not wane since it was already very low. We ran further sensitivity analyses to account for uncertainties in the current knowledge about the (Table ST1.1and Table S2). Relative contact rate once identified and isolated 0·2 (14) No Self-Isolation scenario: 1

S2 Text. Methods and results for the reduced parameter space
To identify more promising ranges of each intervention parameter likely to result in morality rates closer to the desired mortality rate, we changed each value to be between its worst-and best-case scenario values (Table ST2.1) while keeping the other parameters at their best-case scenario values. The value found when the percentage of repetitions having a desired mortality rate first exceeding 95 was set as the lower bound of the reduced range for this parameter.
Only the ranges of ICU, ΔVac. 70above, or Antiviral were cut (Table ST2.2), because they are the only parameters that were able to affect the probability of safe exits (Fig. ST2.1). We did not cut the range for Shenzhen under all scenarios, and Shiyan under the optimistic VE scenario, since they already had high chances of resulting in safe exits in the full space.

S3 Text. Results for the sensitivity analyses
Possibility of a safe exit. We examined the distribution of the mortality rate under different sensitivity scenarios (Fig. ST3.1). When compared the 75% testing with the baseline scenario, the mortality rate increased marginally, but insignificantly, under the best-case intervention scenario, while almost no impacts were observed under the worst-case intervention scenario. Assuming a lower basic reproductive number (R0 = 5 scenario), or higher (R0 = 10 scenario), has almost no influence in the mortality rate under both scenarios. The impacts of the vaccine effectiveness, however, are remarkable with a higher VE (optimistic VE scenario) resulting in significantly lower and a lower VE (pessimistic VE scenario) significantly higher median mortality rates when compared with the baseline scenario. The relative susceptibility of children and adolescents to adults (Lower Child. Sus. scenario), relative infectivity of asymptomatic cases to presymptomatic and symptomatic cases (Lower Asymp. Inf. scenario), reduction in home contact rate after detection (No Self-Isolation scenario), and changes in mortality rate when having no access to hospital beds (5*Hosp. Mort. and 2*Hosp. Mort. scenarios) had no significant impacts on the median mortality rate. Based on these results, the optimistic VE scenario for China, Shanghai and Shiyan, and both the optimistic VE and pessimistic VE scenarios for Shenzhen were included in further analyses, since they resulted in median mortality rates in the desired range and differ significantly from the baseline scenario.

Importance of each intervention in determining the mortality rate.
Under the optimistic VE scenario, 37, 11, 100 and 95 out of the 100 random samples from the full parameter space result in safe exits for China, Shanghai, Shenzhen, and Shiyan, respectively; with average median mortality rates of 18.3, 44.9, 3.67, and 8.17 per 100,000 persons ( Fig. ST3.2A). Under the pessimistic VE scenario, we only examined the results for Shenzhen, since it was only possible for Shenzhen to safely exit the Zero-COVID policy under this scenario. For other locations, safe exits are impossible even under the base-case intervention scenario. For Shenzhen, 95 out of 100 random samples achieve median mortality rates lower than that of influenza, and the average median mortality rate was 6.12 per 100,000 persons. Results from the 10-fold cross-validation suggest that the fitted Gaussian process emulators have strong and robust predictive power, even on the out-of-bag samples that were not used in training the model (Fig. ST3.3). Therefore, we used them to examine the importance of each intervention in determining the mortality rate. The results show that, the same as under the baseline scenario, for all locations, Hospital and ΔVac. 0-19 are the least important intervention parameters in determining the mortality rate, while ΔVac. 70above, Antiviral, and ICU are the most important parameters (Fig. ST3.2B).  Feasible intervention combinations for a safe exit. In the reduced parameter space (see methods and the new ranges in S2 Text), under the optimistic VE scenario, 57 and 46 out of the new 100 sample sets result in safe exits from the Zero-COVID policy for China and Shanghai, respectively. For both locations, ICU, Antiviral and ΔVac. 70above are the most important three parameters (Fig. S8) and were used to fit the simplified GP models. The validation of the simplified models were shown in Fig. S9, with Pearson's correlation coefficients of at least 0.93 across scenarios and locations. We used them to make predictions on a fine grid of the three most important parameters, and estimated the minimal number of ICU beds per 100,000 persons (colors of the pixels in Fig. ST3.4) required for each combination of the other two important intervention parameters (x-and y-axis of Fig. ST3.4) for visualizing the feasible region. There are clear tradeoffs between the three intervention parameters. As the value of one parameter increase, the minimal values of the other two required for a safe exit decrease.
Reaching safe exits are possible, although extremely challenging for China and Shanghai, even under the optimistic VE scenario, which requires very high vaccine coverage, antiviral coverage, or number of ICU beds, or all three. However, it is always possible for Shenzhen under the optimistic VE scenario, but still requires high public health resource investments under the pessimistic VE scenario. For Shiyan under the optimistic VE scenario, reaching a safe exit requires a 62.8% antiviral coverage, or at least 57.2% antiviral coverage, together with a 26% increase in the vaccine coverage among the above 70 years old, or increasing ICU beds to 6.86 per 100,000 persons. The color of a pixel shows the lowest number of ICU beds per 100,000 persons required for a safe exit, while the x-and yaxis show the other two most important intervention parameters. Note that the x-and y-axis and color schemes vary between panels.

Fig. S1
Proportion of population in each age group for each location used for generating the synthetic population. The data were obtained from (16).